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Study shows brain scans could help to identify babies at risk of developing autism

Study shows brain scans could help to identify babies at risk of developing autism

By MiNDFOOD |
February 17, 2017

Study shows brain scans could help to identify babies at risk of developing autism

A new study is raising hope for Autism diagnosis and benefits of early interventions.

Study shows brain scans could help to identify babies at risk of developing autism

Scientists have developed an early stage test that can identify babies, who are most at risk of developing autism, from brain scans in the first year.

The brain scans helped doctors to monitor and diagnose children, who had a family history of autism, and found that tell tale signs were indicative of future diagnosis.

Scientists said that this work raises hopes that affected children could receive specialised care and the appropriate early intervention, before behavioural symptoms began to appear.

106 babies took part in the study, all of whom had an older sibling who had been diagnosed with autism. The scans were taken at six and twelve months, and revealed which infants would go on to be diagnosed with autism.

Although not ready to be used in clinics, the technology used in the study could pave the way for diagnosing high-risk babies in the first year.

The importance of these findings is not lost on the medical community. Such a diagnosis tool is yet to be discovered, and as such, children are having to wait for diagnosis until age 2, 3 or 4 – or even older.

Co-author on the study, Annette Estes, said “Researchers could start developing interventions to prevent these children from falling behind in social and communication skills.”

The test is yet to be trialled on children who are not considered high risk.

Published in the journal Nature, researchers found that the cortical surface area – the measure of the size of folds on the outside of the brain – grew faster in infants who were later diagnosed with autism, compared to those who were not diagnosed.

“The team then used a deep-learning neural network, a form of machine learning, to ask if MRI scans at 6 and 12 months in a larger set of high-risk infants could predict an autism diagnosis at age 2. The algorithm correctly predicted 30 out of the 37 autism diagnoses (81%), while producing false-positives in 4 out of the 142 infants who were not later diagnosed.

“We now have this finding in these high familial risk infants that we can predict 8 out of 10 that we think will get autism,” says psychiatrist Joseph Piven who co-led the study, adding that behaviour-based predictions do no better than 50–50 at that age. “This has tremendous clinical implications.”

“It’s an excellent piece of science, but ultimately it’s based on a few hundred individuals,” says Armin Raznahan, a clinician-scientist at the National Institute of Mental Health in Bethesda, Maryland. “The key thing is going to be replicating this.”

Carol Povey, director of the National Autistic Society’s Centre for Autism, told The Guardian: “If confirmed by further research, it’s possible that MRI scanning of this type could be developed to help families who already have an autistic child to access earlier diagnosis for subsequent children. This would mean those children could receive the right support as early as possible.

“It is important to highlight, however, that autism is a complex condition which manifests in many different ways. No single test is likely to be able to identify potential autism in all children, but this technology could help simplify and speed up the route to autism assessment for some children.”